import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Convolution2D, MaxPooling2D, Flatten  # 二维卷积，二维池化，扁平化
from keras.optimizers import Adam
from keras.utils.vis_utils import plot_model
import matplotlib.pyplot as plt

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(-1, 28, 28, 1) / 255.0
x_test = x_test.reshape(-1, 28, 28, 1) / 255.0

y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

model = Sequential()

# 第一个卷积层
# input_shape 输入平面
# filters 卷积核/滤波器个数
# kernel_size 卷积窗口大小
# strides 步长
# padding padding 方式 same/valid
# activation 激活函数
model.add(Convolution2D(
    input_shape=(28, 28, 1),
    filters=32,
    strides=1,
    kernel_size=5,
    padding='same',
    activation='relu',
))

# 第一个池化层
model.add(MaxPooling2D(
    pool_size=2,
    strides=2,
    padding='same',
))

# 第二个卷积层
model.add(Convolution2D(64, 5, strides=1, padding='same', activation='relu'))

# 第二个池化层
model.add(MaxPooling2D(2, 2, 'same'))

# 把第二个池化层的输出扁平化为1维
model.add(Flatten())

# 第一个全连接层
model.add(Dense(1024, activation='relu'))

# Dropout
model.add(Dropout(0.5))

# 第二个全连接层
model.add(Dense(10, activation='softmax'))

# 第一优化器
adam = Adam(learning_rate=1e-4)

# 定义优化器，loss function，训练过程中就算准确度
model.compile(
    optimizer=adam,
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# # 训练模型
# model.fit(x_train, y_train, batch_size=64, epochs=10)
#
# # 评估模型
# loss, accuracy = model.evaluate(x_test, y_test)
#
# print('test loss:', loss)
# print('test accuracy:', accuracy)

# 绘制网络结构
plot_model(model, to_file='CNN.png', show_shapes=True, show_layer_names=False, rankdir="TB")  # TB从上到下，LR从左到右
plt.figure(figsize=(10, 10))
img = plt.imread('CNN.png')
plt.imshow(img)
plt.axis('off')
plt.show()
